Optimal image guided adaptive radiotherapy requires a 4D representation of the patients anatomy, that allows the position of tumor and normal tissue voxels to be tracked through the processes of biological imaging, planning and simulation, delivery of brachytherapy, and administration of each IMRT fraction. The scientific objective of this project is to investigate novel methods of nonrigid image registration for constructing and validating such representations of the patient's anatomy as it changes during the treatment process. The practical goal is to create a suite of image processing resources that will enable the routine application of image-guided adaptive radiotherapy techniques in the clinic. In specific aim 1, we will investigate contour-driven deformable registration methods for mapping high-dose brachytherapy (HDR) dose distributions in the pelvis to IMRT dose distributions, and for registering biological images to external beam planning images, including development of a novel surface matching algorithm that accounts for contouring uncertainties. To efficiently map information from planning CT images to onboard CT images, acquired prior to administering each daily fraction, we will develop fast parametric image deformation algorithms that do not require manually contoured landmarks. In Specific Aim 2. we will investigate novel methods for reconstructing CT images from incomplete projection data by matching deformation models to sequences of planar image projections, thereby integrating image reconstruction and deformable registration into a single process. This will be used to develop 4D anatomic representations of patient respiration with improved temporal resolution and to estimate intrafraction anatomic deformation from higher temporal resolution sequences of 2D images. Finally, in Specific Aim 3, novel methods for estimating the uncertainty and error of deformable image registration will be developed.